scholarly journals Model-based forecasting for Canadian COVID-19 data

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244536
Author(s):  
Li-Pang Chen ◽  
Qihuang Zhang ◽  
Grace Y. Yi ◽  
Wenqing He
Keyword(s):  
New York ◽  
Time Series Data ◽  
New York State ◽  
Smooth Transition ◽  
World Health ◽  
Series Data ◽  
Short Term ◽  
Model Based ◽  
Star Models ◽  
The Impact

Background Since March 11, 2020 when the World Health Organization (WHO) declared the COVID-19 pandemic, the number of infected cases, the number of deaths, and the number of affected countries have climbed rapidly. To understand the impact of COVID-19 on public health, many studies have been conducted for various countries. To complement the available work, in this article we examine Canadian COVID-19 data for the period of March 18, 2020 to August 16, 2020 with the aim to forecast the dynamic trend in a short term. Method We focus our attention on Canadian data and analyze the four provinces, Ontario, Alberta, British Columbia, and Quebec, which have the most severe situations in Canada. To build predictive models and conduct prediction, we employ three models, smooth transition autoregressive (STAR) models, neural network (NN) models, and susceptible-infected-removed (SIR) models, to fit time series data of confirmed cases in the four provinces separately. In comparison, we also analyze the data of daily infections in two states of USA, Texas and New York state, for the period of March 18, 2020 to August 16, 2020. We emphasize that different models make different assumptions which are basically difficult to validate. Yet invoking different models allows us to examine the data from different angles, thus, helping reveal the underlying trajectory of the development of COVID-19 in Canada. Finding The examinations of the data dated from March 18, 2020 to August 11, 2020 show that the STAR, NN, and SIR models may output different results, though the differences are small in some cases. Prediction over a short term period incurs smaller prediction variability than over a long term period, as expected. The NN method tends to outperform other two methods. All the methods forecast an upward trend in all the four Canadian provinces for the period of August 12, 2020 to August 23, 2020, though the degree varies from method to method. This research offers model-based insights into the pandemic evolvement in Canada.

Editorial: The Impact of Chinese Medicine on America

1992 ◽  
Vol 20 (01) ◽  
pp. 1-16 ◽  
Author(s):  
Frederick F. Kao
Keyword(s):  
New York ◽  
Chinese Medicine ◽  
Traditional Medicine ◽  
Chinese Culture ◽  
New York State ◽  
Panel Member ◽  
The United States ◽  
World Health ◽  
Advisory Panel ◽  
The Impact

As a Chinese American born in Peking and educated both in China and in the United States, the author has, for several decades, been interested in the impact of Chinese culture, including medicine, on American society. While holding a professorship in physiology and biophysics at the State University of New York, Downstate Medical Center, the author began to teach a course on Chinese medical history in the early 1960s. In 1972, he founded the Institute for Advanced Research in Asian Science and Medicine (IARASM) which publishes the American Journal of Chinese Medicine, holds international conferences for scholars and physicians interested in indigenous medical systems, trains physicians for acupuncture therapy, and fosters centers for urban primary health care. The author is a member of the World Health Organization's Expert Advisory Panel on Traditional Medicine. He is the Editor-in-Chief of the American Journal of Chinese Medicine which now reaches an audience in 45 countries. The IARASM is a World Health Organization Collaborating Center for Traditional Medicine. The author served on the Rockefeller Commission of New York State on Acupuncture in 1973, and, in the same year, served as a panel member of the National Institutes of Health Conference on Acupuncture. He visited China at the invitation of the Ministry of Public Health of the People's Republic of China or WHO in 1973, 1974, 1977, 1978, 1979, 1980, 1984, and 1987 when he chaired meetings and lectured to faculty of several medical schools. The author envisages that the process of integration of all indigenous medicines of various cultures will end in the 21st century, at which time the "ecumenical medicine" - a term first used by Joseph Needham - movement will not be necessary, for all forms of medicine will be one system. The author has a great interest in the furtherance of indigenous medicine and their integration into one system, but his views and observations, as all endeavors in humanity, are not infallible.

scholarly journals Car-Following Modeling Incorporating Driving Memory Based on Autoencoder and Long Short-Term Memory Neural Networks

2019 ◽  
Vol 11 (23) ◽  
pp. 6755
Author(s):  
Pengcheng Fan ◽  
Jingqiu Guo ◽  
Haifeng Zhao ◽  
Jasper S. Wijnands ◽  
Yibing Wang
Keyword(s):  
Time Series Data ◽  
Short Term Memory ◽  
Autonomous Driving ◽  
Behavior Modeling ◽  
Series Data ◽  
Short Term ◽  
Term Memory ◽  
Car Following ◽  
Long Short Term Memory ◽  
The Impact

Although a lot of work has been conducted on car-following modeling, model calibration and validation are still a great challenge, especially in the era of autonomous driving. Most challengingly, besides the immediate benefit incurred with a car-following action, a smart vehicle needs to learn to evaluate the long-term benefits and become foresighted in conducting car-following behaviors. Driving memory, which plays a significant role in car-following, has seldom been considered in current models. This paper focuses on the impact of driving memory on car-following behavior, particularly, historical driving memory represents certain types of driving regimes and drivers’ maneuver in coordination with the variety of driving regimes. An autoencoder was used to extract the main features underlying the time-series data in historical driving memory. Long short-term memory (LSTM) neural network model has been employed to investigate the relationship between driving memory and car-following behavior. The results show that velocity, relative velocity, instant perception time (IPT), and time gap are the most relevant parameters, while distance gap is insignificant. Furthermore, we compared the accuracy and robustness of three patterns including various driving memory information and span levels. This study contributes to bridging the gap between historical driving memory and car-following behavior modeling. The developed LSTM methodology has the potential to provide personalized warnings of dangerous car-following distance over the next second.

scholarly journals Assessing the impact of data aggregation in model predictions of HAT transmission and control activities

2019 ◽  
Author(s):  
M. Soledad Castaño ◽  
Martial L. Ndeffo-Mbah ◽  
Kat S. Rock ◽  
Cody Palmer ◽  
Edward Knock ◽  
...  
Keyword(s):  
Mathematical Models ◽  
Data Aggregation ◽  
Time Series Data ◽  
Public Health Problem ◽  
Sleeping Sickness ◽  
Transmission Dynamics ◽  
World Health ◽  
Disease Stage ◽  
Series Data ◽  
The Impact

AbstractSince the turn of the century, the global community has made great progress towards the elimination of gambiense human African trypanosomiasis (HAT). Elimination programs, primarily relying on screening and treatment campaigns, have also created a rich database of HAT epidemiology. Mathematical models calibrated with these data can help to fill remaining gaps in our understanding of HAT transmission dynamics, including key operational research questions such as whether integrating vector control with current intervention strategies is needed to achieve HAT elimination. Here we explore, via an ensemble of models and simulation studies, which aspects of the available data and level of data aggregation, such as separation by disease stage, would be most useful for better understanding transmission dynamics and improving model reliability in making future predictions of control and elimination strategies.Author summaryHuman African tryposonomiasis (HAT), also known as sleeping sickness, is a parasitic disease with over 65 million people estimated to be living at risk of infection. Sleeping sickness consists of two stages: the first one is relatively mild but the second stage is usually fatal if untreated. The World Health Organization has targeted HAT for elimination as a public health problem by 2020 and for elimination of transmission by 2030. Regular monitoring updates indicate that 2020 elimination goals are likely to be achieved. This monitoring relies mainly on case report data that is collected through medical-based control activities — the main strategy employed so far in HAT control. This epidemiological data are also used to calibrate mathematical models that can be used to analyse current interventions and provide projections of potential intensified strategies.We investigated the role of the type and level of aggregation of this HAT case data on model calibrations and projections. We highlight that the lack of detailed epidemiological information, such as missing stage of disease or truncated time series data, impacts model recommendations for strategy choice: it can misrepresent the underlying HAT epidemiology (for example, the ratio of stage 1 to stage 2 cases) and increase uncertainty in predictions. Consistently including new data from control activities as well as enriching it through cross-sectional (e.g. demographic or behavioural data) and geo-located data is likely to improve modelling accuracy to support planning, monitoring and adapting HAT interventions.

Examining the Impact on Road Safety Performance of Socioeconomic Variables in Turkey

2021 ◽  
pp. 036119812110363
Author(s):  
Salih Bektaş
Keyword(s):  
Energy Consumption ◽  
Traffic Accidents ◽  
Positive Relationship ◽  
World Health ◽  
Motor Vehicles ◽  
Series Data ◽  
Short Term ◽  
Short And Long Term ◽  
The Impact

The World Health Organization (WHO) has reported that each year, 1.35 million people worldwide die in traffic accidents, 20 to 50 million people are injured, and many of those who are injured are disabled. This article uses time-series data for the period 1970 to 2018 in Turkey short- and long-term social economic variables between the number of road accidents, energy consumption, gross domestic product per capita, vehicle kilometers traveled, number of motor vehicles, divided road length, and population growth to investigate the causal relationship. In the analysis, the vector error correction model (VECM) and the autoregressive distributed lag (ARDL) model were used for the short and long term, respectively. The results show that a 1% increase in the number of motor vehicles increases the number of accidents by 2.83% in the long term and has a positive relationship with traffic accidents. It has been determined that a 1% increase in the population increases the number of accidents by 9.43% in the short term and has a positive relationship with traffic accidents. It has been observed that a 1% increase in the length of the divided highway (LNDR [-2]) reduces accidents by 1.21% in the short term and there is a negative relationship between energy consumption and divided roads. This result supports the decision of the administrators in the country to construct a divided road.

scholarly journals Using patient biomarker time series to determine mortality risk in hospitalised COVID-19 patients: a comparative analysis across two New York hospitals

2021 ◽  
Author(s):  
Ben Lambert ◽  
Isaac J. Stopard ◽  
Amir Momeni-Boroujeni ◽  
Rachelle Mendoza ◽  
Alejandro Zuretti
Keyword(s):  
New York ◽  
Time Series ◽  
Mortality Risk ◽  
Time Series Data ◽  
Medical Center ◽  
New York State ◽  
Prognostic Models ◽  
Series Data ◽  
Clinical Measurements ◽  
The Individual

AbstractA large range of prognostic models for determining the risk of COVID-19 patient mortality exist, but these typically restrict the set of biomarkers considered to measurements available at patient admission. Additionally, many of these models are trained and tested on patient cohorts from a single hospital, raising questions about the generalisability of results. We used a Bayesian Markov model to analyse time series data of biomarker measurements taken throughout the duration of a COVID-19 patient’s hospitalisation for n = 1540 patients from two hospitals in New York: State University of New York (SUNY) Downstate Health Sciences University and Maimonides Medical Center. In doing so, we quantified the mortality risk associated with both static (e.g. demographic and patient history variables) and dynamic factors (e.g. changes in biomarkers) throughout hospitalisation. By using our model to make predictions across the hospitals, we assessed how predictive factors generalised between the two cohorts. The individual dynamics of the measurements and their associated mortality risk were remarkably consistent across the hospitals. The model accuracy in predicting patient outcome (death or discharge) was 72.3% (predicting SUNY; posterior median accuracy) and 71.4% (predicting Maimonides) respectively. Model sensitivity was higher for detecting patients who would go on to be discharged (79.2%) versus those who died (61.0%). Our results indicate the utility of including dynamic clinical measurements when assessing patient mortality risk but also highlight the difficulty of identifying high risk patients.

scholarly journals The Impact of Short- Term Interest Rates on Bank Funding Costs

2018 ◽  
Vol 10 (3(J)) ◽  
pp. 141-148
Author(s):  
Azasakhe Nkcubeko Nomsobo ◽  
Roscoe Bertrum Van Wyk
Keyword(s):  
Interest Rates ◽  
Financial Stability ◽  
Time Series Data ◽  
Series Data ◽  
Bank Capital ◽  
Short Term ◽  
Government Bonds ◽  
Bank Funding ◽  
The Impact

This study examines the impact of short- term interest rates on bank funding costs in South Africa. Literature suggests that rising short- term interest rates may cause similar financial crises experienced in 2007/08 (Bonner & Eijffinger, 2013; Turner, 2013; Saraç & Karagoz, 2016). It is vital to study short- term interest rates and bank funding costs in order to achieve financial stability. The study uses quarterly time series data for the period 2000 to 2014. To estimate the regression, the study uses the Vector Autoregressive model (VAR) and the data is found stationary at first difference. The 3 months Johannesburg Interbank Agreed Rate (JIBAR) is used as a proxy for bank funding costs whilst the prime overdraft rate, 10 -year government bonds and capital ratio are used as proxies for short- term, long- term interest rates and bank capital, respectively. The results show a positive and significant long- term relationship between the variables. The results for prime overdraft rate, 10 -year government bonds and capital ratio conform to the apriori expectations. For GDP growth the results show a positive relationship which does not conform to apriori expectations. Using the variance decomposition, the study illustrates fluctuations in JIBAR was due to changes in its value and fluctuations in the prime rate are also due to JIBAR. The study presents policy options whereby regulatory efforts need to strengthen the capital buffers of banks to reduce bank funding costs and therefore reduce short- term interest rates imposed on borrowers.

scholarly journals HOW TENURE OF SUKUK ISSUANCES IMPACTS THE ECONOMIC GROWTH IN MALAYSIA?

2019 ◽  
Vol 1 (1) ◽  
pp. 01-12
Author(s):  
Siti Sarah Mohd Zaki Fadzil ◽  
Noraziah Che Arshad
Keyword(s):  
Economic Growth ◽  
Central Bank ◽  
Positive Relationship ◽  
Time Series Data ◽  
Negative Relationship ◽  
Unit Root Test ◽  
Series Data ◽  
Short Term ◽  
The Impact

The present paper analyses the impact of Sukuk issuances on the economic growth of Malaysia over a period of 10 years from 2008 to 2017 on a yearly basis. There are six different types of Sukuk issuances which includes the long-term government/treasury/central bank (LGTC), long-term corporate (LCTE), long-term agency (LAGY), short-term government/treasury/central bank (SGTC), short-term corporate (SCTE) and short-term agency (SAGY) with the presences of the moderating variable which is the exchange rate (ER). The 10 years’ time-series data were analyzed by using the diagnostic test, unit root test and multiple regression analysis. The outcome of the study indicates that the presence of the ER, LCTE, SGTC, SCTE, and SAGY found to have a significant and positive relationship with the economic growth (GDP) of Malaysia. However, LGTC found not to be significant but shows a positive relationship with the GDP in Malaysia, whilst LAGY is found to be significant but shows a negative relationship with the GDP in Malaysia. Therefore, the Sukuk issuances give an impact on the economic growth of Malaysia, whereby with the presences of the moderating variable, the long-term and short-term Sukuk issuances can spur the economic growth of Malaysia.

Impact of exchange rate and oil price on the yield of sovereign bond and sukuk: Evidence from Malaysian capital market

2017 ◽  
Vol 5 (4) ◽  
pp. 27
Author(s):  
Huda Arshad ◽  
Ruhaini Muda ◽  
Ismah Osman
Keyword(s):  
Exchange Rate ◽  
Capital Market ◽  
Time Series Data ◽  
Oil Prices ◽  
Oil Price ◽  
Series Data ◽  
Sovereign Bond ◽  
Long Run ◽  
Short Run ◽  
The Impact

This study analyses the impact of exchange rate and oil prices on the yield of sovereign bond and sukuk for Malaysian capital market. This study aims to ascertain the effect of weakening Malaysian Ringgit and declining of crude oil price on the fixed income investors in the emerging capital market. This study utilises daily time series data of Malaysian exchange rate, oil price and the yield of Malaysian sovereign bond and sukuk from year 2006 until 2015. The findings show that the weakening of exchange rate and oil prices contribute different impacts in the short and long run. In the short run, the exchange rate and oil prices does not have a direct relation with the yield of sovereign bond and sukuk. However, in the long run, the result reveals that there is a significant relationship between exchange rate and oil prices on the yield of sovereign bond and sukuk. It is evident that only a unidirectional causality relation is present between exchange rate and oil price towards selected yield of Malaysian sovereign bond and sukuk. This study provides numerical and empirical insights on issues relating to capital market that supports public authorities and private institutions on their decision and policymaking process.

Transport infrastructure of the Rostov Oblast: Problems and prospects for development in the context of national projects implementation

2020 ◽  
Vol 19 (6) ◽  
pp. 1015-1034
Author(s):  
O.Yu. Patrakeeva
Keyword(s):  
Time Series Data ◽  
Mutual Influence ◽  
Rostov Oblast ◽  
Transport Infrastructure ◽  
Series Data ◽  
Cointegration Tests ◽  
Level Of Economic Development ◽  
Short Run ◽  
The Government ◽  
The Impact

Subject. The paper considers national projects in the field of transport infrastructure, i.e. Safe and High-quality Roads and Comprehensive Plan for Modernization and Expansion of Trunk Infrastructure, and the specifics of their implementation in the Rostov Oblast. Objectives. The aim is to conduct a statistical assessment of the impact of transport infrastructure on the region’s economic performance and define prospects for and risks of the implementation of national infrastructure projects in conditions of a shrinking economy. Methods. I use available statistics and apply methods and approaches with time-series data, namely stationarity and cointegration tests, vector autoregression models. Results. The level of economic development has an impact on transport infrastructure in the short run. However, the mutual influence has not been statistically confirmed. The paper revealed that investments in the sphere of transport reduce risk of accidents on the roads of the Rostov Oblast. Improving the quality of roads with high traffic flow by reducing investments in the maintenance of subsidiary roads enables to decrease accident rate on the whole. Conclusions. In conditions of economy shrinking caused by the complex epidemiological situation and measures aimed at minimizing the spread of coronavirus, it is crucial to create a solid foundation for further economic recovery. At the government level, it is decided to continue implementing national projects as significant tools for recovery growth.

Producers' Response to Changes in Prices and Marketing Policies: A Case Study of Sugarcane and Paddy in Eastern U.P. by S. C. Gupta and A. Majid. New York: Asia Publishing House, 1965.

1968 ◽  
Vol 8 (2) ◽  
pp. 308-309
Author(s):  
Mohammad Irshad Khan
Keyword(s):  
New York ◽  
Time Series ◽  
Time Series Data ◽  
Publishing House ◽  
Economic Incentives ◽  
The Other ◽  
Series Data ◽  
Poor Countries ◽  
Northern India

It is alleged that the agricultural output in poor countries responds very little to movements in prices and costs because of subsistence-oriented produc¬tion and self-produced inputs. The work of Gupta and Majid is concerned with the empirical verification of the responsiveness of farmers to prices and marketing policies in a backward region. The authors' analysis of the respon¬siveness of farmers to economic incentives is based on two sets of data (concern¬ing sugarcane, cash crop, and paddy, subsistence crop) collected from the district of Deoria in Eastern U.P. (Utter Pradesh) a chronically foodgrain deficit region in northern India. In one set, they have aggregate time-series data at district level and, in the other, they have obtained data from a survey of five villages selected from 170 villages around Padrauna town in Deoria.

Sign in / Sign up

Export Citation Format

Share Document